Gain insights into simplifying machine learning with PyCaret. Delve into regression, classification, clustering, and anomaly detection, and learn to deploy applications using Streamlit.

Simplifying Machine Learning with PyCaret_ .png

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PyCaret is an low-code, open-source, machine learning library for Python. It can be used in machine learning tasks such as data preparation and model deployment.

In this course, you will learn multiple topics related to machine learning. You will start with a brief introduction to the basic concepts of machine learning, and then continue with case studies of regression, classification, clustering, and anomaly detection based on the respective modules of the PyCaret library. Finally, we will focus on using the Streamlit library to develop and deploy machine learning applications.

By the end of this course, you will have the skills to deploy the robust PyCaret library for any of your machine learning projects.

Simplifying Machine Learning with PyCaret in Python

# Histogram
The `hist()` pandas function lets us easily visualize the distribution of each variable. 

# Plotting histogram

data.hist(bins = 30, figsize = (10,7), grid = False) 
plt.show()

We can see in the output that all variable distributions are either *bimodal* or *multimodal*, meaning they have two or more peaks respectively. This typically happens when the dataset contains multiple groups with different characteristics. In this case, the dataset was specifically created to contain $3$ distinct clusters, so it is reasonable for the variables to have multimodal distributions.

# Color-encoded matrix
We used the `corr()` pandas function and the `heatmap()` Seaborn function to create a heatmap that visualizes the correlation values of all variable pairs.

Learn how to perform exploratory data analysis for clustering on our dataset and plot data on histograms, color-encoded matrices, and scatter plots.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Exploratory Data Analysis

Histogram